Embodiments of the present disclosure relate generally to hyper-spectral imaging and detection, and more particularly relate to hyper-spectral target detection. Hyper-spectral imaging and detection is a type of spectral imaging and analysis that generally utilizes a wide not-necessarily contiguous band of electromagnetic spectrum for imaging and/or detection of objects. Hyper-spectral remote sensing is used in a wide array of applications, for example, mining and geology where it can be used to look for oil and minerals. Hyper-spectral imaging is also used in fields as widespread as ecology and surveillance, airport security, as well as historical manuscript research such as for imaging ancient illegible texts. There are also automotive applications such as for collision avoidance. Hyper-spectral imaging technology is also relevant to various target detection applications, such as intelligence, surveillance, and reconnaissance (ISR) applications using unmanned aerial vehicles (UAVs).
A hyper-spectral imaging and detection system receives hyper-spectral image data from one or more sensors. The hyper-spectral image data generally contains hundreds to thousands of spectrum bands. Hyper-spectral sensors generally collect information as a set of images, where each image is a two-dimensional array of pixels. Each pixel measures and represents received energy in a range of the electromagnetic spectrum (spectral band). These images are then combined as planes to form a three-dimensional hyper-spectral cube where depth represents a pixel array plane for each spectral band.
Precision of the one or more sensors is typically measured in spectral resolution, where spectral resolution is a bandwidth of each band of the spectrum that is captured by the one or more sensors. If the hyper-spectral imaging and detection system receives a large number of fairly narrow frequency bands, it is possible to identify objects even if the objects are captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution. If a pixel receives spectra from too large a surface area, then multiple objects can be captured in the same pixel, which could make identifying a target more difficult. If the pixels are too small, then the energy captured by each sensor-cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features.
Various algorithms exist to identify target material from background and/or unknown material within a pixel. Generally, the algorithms require high speed, high dynamic-range analog to digital conversion (digitization) of analog signals upfront, followed by highly complex and computationally-intensive digital signal processing (DSP). Both digitization and DSP operations can be very power consuming. Existing algorithm implementations in hardware, such as conventional analog signal processing, may not be optimal due to performance limitations of, for example, the analog circuits using scaled Complementary Metal-Oxide Semiconductor (CMOS) technologies. Conversely, traditional DSP approaches require high-speed sampling and a priori digitization, that may be limited by high power consumption and resulting low computational speed. Traditional hyper-spectral target detection algorithms, such as existing constrained energy minimization (CEM) algorithm implementations, may not be capable of real-time or near real-time performance, especially onboard small power-restricted mobile platforms.